(AI Studio Core)
Synopsis
This metaclassifier makes its base classifier cost-sensitive by using the given cost matrix to compute label predictions according to classification costs.Description
The MetaCost operator makes its base classifier cost-sensitive by using the cost matrix specified in the cost matrix parameter. The method used by this operator is similar to the MetaCost method described by Pedro Domingos (1999).
The MetaCost operator is a nested operator i.e. it has a subprocess. The subprocess must have a learner i.e. an operator that expects an ExampleSet and generates a model. This operator tries to build a better model using the learner provided in its subprocess. You need to have basic understanding of subprocesses in order to apply this operator. Please study the documentation of the Subprocess operator for basic understanding of subprocesses.
Most classification algorithms assume that all errors have the same cost, which is seldom the case. For example, in database marketing the cost of mailing to a non-respondent is very small, but the cost of not mailing to someone who would respond is the entire profit lost. In general, misclassification costs may be described by an arbitrary cost matrix C, with C(i,j) being the cost of predicting that an example belongs to class i when in fact it belongs to class j. Individually making each classification learner cost-sensitive is laborious, and often non-trivial. MetaCost is a principled method for making an arbitrary classifier cost-sensitive by wrapping a cost-minimizing procedure around it. This procedure treats the underlying classifier as a black box, requiring no knowledge of its functioning or change to it. MetaCost is applicable to any number of classes and to arbitrary cost matrices.
Input
training set (Data table)This input port expects an ExampleSet. It is the output of the Retrieve operator in the attached Example Process. The output of other operators can also be used as input.
Output
model (MetaCost Model)The meta model is delivered from this output port which can now be applied on unseen data sets for prediction of the label attribute.
- example set (Data table)
The ExampleSet that was given as input is passed without changing to the output through this port. This is usually used to reuse the same ExampleSet in further operators or to view the ExampleSet in the Results Workspace.
Parameters
- cost matrixThis parameter is used for specifying the cost matrix. The cost matrix is similar in structure to a confusion matrix because it has predicted classes in one dimension and actual classes on the other dimension. Therefore the cost matrix can denote the costs for every possible classification outcome: predicted label vs. actual label. Actually this matrix is a matrix of misclassification costs because you can specify different weights for certain classes misclassified as other classes. Weights can also be assigned to correct classifications but usually they are set to 0. The classes in the matrix are labeled as Class 1, Class 2 etc where classes are numbered according to their order in the internal mapping.
- use subset for trainingThis parameter specifies the fraction of examples to be used for training. Its value must be greater than 0 (i.e. zero examples) and should be lower than or equal to 1 (i.e. entire data set).
- iterationsThis parameter specifies the maximum number of iterations of the MetaCost algorithm.
- sampling with replacementThis parameter indicates if sampling with replacement should be used. In sampling with replacement, at every step all examples have equal probability of being selected. Once an example has been selected for the sample, it remains candidate for selection and it can be selected again in any other coming steps. Thus a sample with replacement can have the same example multiple number of times.
- use local random seedThis parameter indicates if a local random seed should be used for randomization. Using the same value of local random seed will produce the same sample. Changing the value of this parameter changes the way examples are randomized, thus the sample will have a different set of values.
- local random seedThis parameter specifies the local random seed. This parameter is only available if the use local random seed parameter is set to true.
Tutorial Processes
Using the MetaCost operator for generating a better Decision Tree
The 'Sonar' data set is loaded using the Retrieve operator. The Split Validation operator is applied on it for training and testing a classification model. The MetaCost operator is applied in the training subprocess of the Split Validation operator. The Decision Tree operator is applied in the subprocess of the MetaCost operator. The iterations parameter of the MetaCost operator is set to 10, thus there will be 10 iterations of its subprocess. Have a look at the cost matrix specified in the cost matrix parameter of the MetaCost operator. You can see that the misclassification costs are not equal. The Apply Model operator is used in the testing subprocess for applying the model generated by the MetaCost operator. The resultant labeled ExampleSet is used by the Performance (Classification) operator for measuring the performance of the model. The classification model and its performance vector are connected to the output and they can be seen in the Results Workspace. You can see that the MetaCost operator produced a new model in each iteration. The accuracy of this model turns out to be around 82%. If the same process is repeated without MetaCost operator i.e. only Decision Tree operator is used in training subprocess then the accuracy of that model turns out to be around 66%. Thus MetaCost improved the performance of the base learner (i.e. Decision Tree) by using the cost matrix to compute label predictions according to classification costs.
